cad2data-Revit-IFC-DWG-DGN — security grade SAFE, quality 60/100

Security audit verdict: SAFE · quality 60/100

No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →

by datadrivenconstruction · Agent Tool · ★ 452

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

🔒 Is cad2data-Revit-IFC-DWG-DGN safe to install? View the security audit →

About cad2data-Revit-IFC-DWG-DGN

🇬🇧 English • 🇩🇪 Deutsch • 🇪🇸 Español • 🇫🇷 Français • 🇷🇺 Русский • 🇨🇳 中文 • 🇸🇦 العربية <img alt="Static Badge" src="https://img.shields.io

ai-agentsantigravityautocadautodeskbimbimconvertercadcadconverterclaude-codeconverter

Quick Facts

Stars452
Forks105
LanguageJupyter Notebook
CategoryAgent Tool
Quality Score60.28581604662/100
Open Issues2
Last Updated2026-06-22
Created2025-06-19
Platformsclaude-code
Est. Tokens~28k

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Frequently Asked Questions

What is cad2data-Revit-IFC-DWG-DGN?

cad2data-Revit-IFC-DWG-DGN is Workflow for AI Agents enables automated conversion of CAD files (such as `.rvt`, `.ifc`, `.dwg`) using command-line converters on a local Windows machine. It is categorized as a Agent Tool with 452 GitHub stars.

What programming language is cad2data-Revit-IFC-DWG-DGN written in?

cad2data-Revit-IFC-DWG-DGN is primarily written in Jupyter Notebook. It covers topics such as ai-agents, antigravity, autocad.

How do I install or use cad2data-Revit-IFC-DWG-DGN?

You can find installation instructions and usage details in the cad2data-Revit-IFC-DWG-DGN GitHub repository at github.com/datadrivenconstruction/cad2data-Revit-IFC-DWG-DGN. The project has 452 stars and 105 forks, indicating an active community.

What are the best alternatives to cad2data-Revit-IFC-DWG-DGN?

The top alternatives to cad2data-Revit-IFC-DWG-DGN on Agent Skills Hub include ai-devkit, antigravity-skills, hcom. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.

How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.

The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.

Sources & who's responsible:

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